Computational approaches for the discovery of bacterial small RNAs

Recent work has uncovered a growing number of bacterial small RNAs (sRNAs), some of which have been shown to regulate critical cellular processes. Computational approaches, in combination with experiments, have played an important role in the discovery of these sRNAs. In this article, we first give an overview of different computational approaches for genome-wide prediction of sRNAs. These approaches have led to the discovery of several novel sRNAs, however the regulatory roles are not yet known for a majority of these sRNAs. By contrast, several recent studies have highlighted the inverse problem where the functional role of the sRNA is already known and the challenge is to identify its genomic location. The focus of this article is on computational tools and strategies for identifying these specific sRNAs which function as key components of known regulatory pathways.     

Genetic and genomic approaches to study placental development

Recent technological advances in genetic manipulation and expression profiling offer excellent opportunities to elucidate the molecular mechanisms controlling developmental processes during embryogenesis. Thus, this revolution also strongly benefits studies of the molecular genetics of placental development. Here we review the findings of several expression profiling analyses in extraembryonic tissues and assess how this work can contribute to the identification of essential components governing placental development. We further discuss the relevance of these components in the context of genetic manipulation experiments. In conclusion, the intelligent combination of genetic and genomic approaches will substantially accelerate the progress in identifying the key molecular pathways of placental development.     

Computational approaches to gene prediction

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.     

Computational approaches to motor control

New concepts and computational models that integrate behavioral and neurophysiological observations have addressed several of the most fundamental long-standing problems in motor control. These problems include the selection of particular trajectories among the large number of possibilities, the solution of inverse kinematics and dynamics problems, motor adaptation and the learning of sequential behaviors.     

microRNA计算发现方法的研究进展

microRNA (miRNA)是近几年发现的一类长度为～21 nt的内源非编码小RNA, 在植物和动物中发挥着重要而广泛的调控功能。它的发现主要有cDNA克隆测序和计算发现两条途径。由于cDNA克隆测序方法受miRNA表达的时间和组织特异性以及表达水平的影响, 而计算发现可以弥补其不足, 因此miRNA的计算发现方法研究受到了广泛的重视。文章对近几年计算发现miRNA的研究进展进行了综述, 根据计算发现方法的本质, 将计算发现方法归纳为5类, 分别是同源片段搜索方法、基于比较基因组学的预测方法、基于序列和结构特征打分的预测方法、结合作用靶标的预测方法和基于机器学习的预测方法, 并对各类方法的原理、核心思想、优点和局限性进行了分析, 最后探讨了进一步的发展方向。     

Computational approaches to molecular recognition

Recent advances in the computation of free energies have facilitated the understanding of host—guest and protein—ligand recognition. Rigorous perturbation methods have been assessed and expanded, and more approximate techniques have been developed that allow faster treatment of diverse systems.     

Computational approaches to protein-protein interaction

The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.     

Evaluation of the use of S1 nuclease to detect small length variations in genomic DNA

A method which utilises S1 nuclease to detect small length variations in cloned and genomic DNA has been evaluated. The methodology of this technique is simple and robust, permitting the rapid analysis of 10(4) base pairs. By employing defined sequence variants, this method is shown to have a sensitivity which should enable the detection of length variations of only a few base pairs in heterozygous individuals.     

Computational approaches to neuronal network analysis

Computational modelling is an approach to neuronal network analysis that can complement experimental approaches. Construction of useful neuron and network models is often complicated by a variety of factors and unknowns, most notably the considerable variability of cellular and synaptic properties and electrical activity characteristics found even in relatively ‘simple’ networks of identifiable neurons. This chapter discusses the consequences of biological variability for network modelling and analysis, describes a way to embrace variability through ensemble modelling and summarizes recent findings obtained experimentally and through ensemble modelling.     

Computational approaches to hippocampal function.

The results of theoretical work have led researchers to suggest that the hippocampal formation may maximize its memory storage capacity by recoding events into patterns that are as dissimilar to one another, and which use as few neurons per event, as possible.     

Genetic and genomic approaches to glomerulosclerosis

Chronic kidney disease (CKD) is common, progressive and expensive to manage. Although modifiable risk factors can be treated and outcomes improved, CKD remains a chronic disease with excessive morbidity and mortality. The completion of the human genome sequence and the advent of methodologies to define gene function provide new opportunities to manage and treat patients with CKD and other chronic diseases. Despite the lack of clear correspondence between genotype and phenotype and an obvious Mendelian inheritance pattern, CKD susceptibility has a genetic basis. In this review, we focus on recent studies of familial focal segmental glomerulosclerosis and the discoveries that have resulted from both genetic and genomic approaches used to understand its pathogenesis. Key slit diaphragm proteins were discovered using linkage analyses of these rare causes of glomerulosclerosis and subsequent work has characterized slit diaphragm function in health and disease. Podocyte dysfunction is now recognized as a key contributor to the functional and histologic derangements that characterize glomerular dysfunction in many common causes of CKD. In aggregate, these studies provide a paradigm for approaches to better define mechanisms of CKD and to identify novel therapeutic targets.     

Computational approaches to identify leucine zippers.

The leucine zipper is a dimerization domain occurring mostly in regulatory and thus in many oncogenic proteins. The leucine repeat in the sequence has been traditionally used for identification, however with poor reliability. The coiled coil structure of a leucine zipper is required for dimerization and can be predicted with reasonable accuracy by existing algorithms. We exploit this fact for identification of leucine zippers from sequence alone. We present a program, 2ZIP, which combines a standard coiled coil prediction algorithm with an approximate search for the characteristic leucine repeat. No further information from homologues is required for prediction. This approach improves significantly over existing methods, especially in that the coiled coil prediction turns out to be highly informative and avoids large numbers of false positives. Many problems in predicting zippers or assessing prediction results stem from wrong sequence annotations in the database.     

Computational approaches to motor learning by imitation

Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees-of-freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking-indeed, one could argue that we need to understand the complete perception-action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions.     

Computational approaches to unveiling ancient genome duplications

Recent analyses of complete genome sequences have revealed that many genomes have been duplicated in their evolutionary past. Such events have been associated with important biological transitions, major leaps in evolution and adaptive radiations of species. Here, we consider recently developed computational methods to detect such ancient large-scale gene duplication events. Several new approaches have been used to show that large-scale gene duplications are more common than previously thought.     

Computational approaches to study protein unfolding: Hen egg white lysozyme as a case study

Four methods are compared to drive the unfolding of a protein: (1) high temperature (T-run), (2) high pressure (P-run), (3) by imposing a gradual increase in the mean radius of the protein using a penalty function added to the physical interaction function (F-run, radial force driven unfolding), and (4) by weak coupling of the difference between the temperature of the radially outward moving atoms and the radially inward moving atoms to an external temperature bath (K-run, kinetic energy driven unfolding). The characteristic features of the four unfolding pathways are analyzed in order to detect distortions due to the size or the type of the applied perturbation, as well as the features that are common to all of them. Hen egg white lysozyme is used as a test system. The simulations are analyzed and compared to experimental data like ¹H-NMR amide proton exchange-folding competition, heat capacity, and compressibility measurements. © 1995 Wiley-Liss, Inc.